论文标题
使用生成的基因编程暗示模型的动态对冲
Dynamic Hedging using Generated Genetic Programming Implied Volatility Models
论文作者
论文摘要
本文的目的是使用遗传编程产生的隐含波动提高动态对冲的准确性。使用来自S＆P500指数选项的真实数据，在静态和动态训练 - 积分选择方法之间比较了遗传编程预测黑色和隐含波动率的能力。在动态对冲中测试了最佳生成的GP隐含波动的性能，并与黑色choles模型进行了比较。基于MSE的总数，GP的动态训练比带有固定样品的静态训练获得的结果更好。根据对冲错误，GP模型在所有对冲策略中几乎都比BS模型更准确，尤其是对于内在的呼叫选项和货币货币投票选项。
The purpose of this paper is to improve the accuracy of dynamic hedging using implied volatilities generated by genetic programming. Using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes implied volatility is compared between static and dynamic training-subset selection methods. The performance of the best generated GP implied volatilities is tested in dynamic hedging and compared with Black-Scholes model. Based on MSE total, the dynamic training of GP yields better results than those obtained from static training with fixed samples. According to hedging errors, the GP model is more accurate almost in all hedging strategies than the BS model, particularly for in-the-money call options and at-the-money put options.
